Based out of London and San Francisco, Sust Global is a climate data startup that transforms complex climate science into credible climate data. In a recent development, the startup has picked up $3.2 million (nearly £2.3 million) in seed funding.
Growth plans ahead
The investment round was led by Hambro Perks along with investment from Vala Capital, Powerhouse Ventures, Thirdstream Partners, and angel investors from leading UK and US financial firms.
Sust Global will use this funding to grow the size of its commercial and technical teams and to expand its climate product into new markets such as real estate and banking.
Josh Gilbert, Sust Global CEO, said, In an increasingly crowded climate data sector, our geospatial-first approach to climate risk data and emissions insights is truly unique. As our recent customer traction indicates, existing data on climate impacts is often inaccessible. We’re thrilled to secure this investment to further our mission to deliver credible climate data to business and finance.”
Gopal Erinjippurath, CTO & Head of Product at Sust Global, said, “We see a massive opportunity and an unmet need for data-driven interpretation of the latest climate models. Through fusing data sources across different time scales, we are developing the essential inputs and data transformation tools for more sustainable, climate aware capital allocation.”
Founded by Josh Gilbert, Sust Global uses a ‘geospatial-first’ approach to differentiate in the crowded climate data space. Using deep learning techniques to transform climate models, satellite and geospatial data, the company has created a one-stop-shop for physical climate risk and emissions insights. Customers, including global data providers, investors and corporates, can access insights via a cloud-native analytics product.
It builds a geospatial product to solve today’s climate data issues. The company has built product capabilities that deliver high-resolution climate risk data and near real-time emissions insights across global assets.
Sust Global’s approach is novel in its integration of multiple geospatial datasets. Their product integrates historic and near real-time data from satellites and ground based sensors, coupled with forward-looking global climate models, using novel spatial statistics and deep learning techniques.